Neural Network-Based Diesel Engine Emissions Prediction Using In-Cylinder Combustion Pressure West Virginia University

Format:

Conference/Event

Author/Creator:

Traver, Michael L., author.

Conference Name:

International Fuels and Lubricants Meeting and Exposition (1999-05-03 : Dearborn, Michigan, United States)

Language:

English

Physical Description:

1 online resource

Place of Publication:

Warrendale, PA SAE International 1999

Summary:

This paper explores the feasibility of using in-cylinder pressure-based variables to predict gaseous exhaust emissions levels from a Navistar T444 direct injection diesel engine through the use of neural networks. The networks were trained using in-cylinder pressure derived variables generated at steady state conditions over a wide speed and load test matrix. The networks were then validated on previously "unseen" real-time data obtained from the Federal Test Procedure cycle through the use of a high speed digital signal processor data acquisition system. Once fully trained, the DSP-based system developed in this work allows the real-time prediction of NOX and CO2 emissions from this engine on a cycle-by-cycle basis without requiring emissions measurement

Notes:

Vendor supplied data

Publisher Number:

1999-01-1532

Access Restriction:

Restricted for use by site license